Monitoring access of network darkspace

ABSTRACT

A system includes one or more “BotMagnet” modules that are exposed to infection by malicious code. The BotMagnets may include one or more virtual machines hosing operating systems in which malicious code may be installed and executed without exposing sensitive data or other parts of a network. In particular, outbound traffic may be transmitted to a Sinkhole module that implements a service requested by the outbound traffic and transmits responses to the malicious code executing within the BotMagnet. Dark space in a network (unused IP addresses, unused ports and absent applications, and invalid usernames and passwords) is consumed by a BotSink such that attempts to access Darkspace resources will be directed to the BotSink, which will engage the source host of such attempts.

BACKGROUND

In information technology (IT) and networking, the word “Bot” is derivedfrom “robot” and refers to an automated process that interacts withother network elements. Bots may be configured to automate tasks thatwould otherwise be conducted by a human being. A growing problem is theuse of Bots by malicious entities to attack and gain unauthorized accessto network-connected computers and other network resources via theInternet.

One type of Bot process may initially run on a computer controlled bythe malicious entity. It may probe victim networks and computers forvulnerabilities, and upon finding such, exploits them to accessinformation, often personal information of individuals stored incomputers. A Bot may install a program known as “malware” on a victimcomputer merely for the malicious purpose of randomly displaying rudemessages or perhaps even damaging the victim's file system. The malwareprogram may then perform one or more automated processes, which itselfmay be a type of Bot.

In recent years, Bot exploits have become much more sophisticated andfinancially rewarding for the malicious entities. For example, themodern Bots may be programmed to access the victim's computer andsurreptitiously access certain websites and click on advertisements thatare displayed there. In “pay per click” type advertising, each clickfrom a potential buyer generates revenue for the displaying website.Thus, clicks generated by the Bot could create undeserved revenue forthe displaying website. In the art, this is called “click-fraud.”

The problem is greatly compounded by the fact that Bots on a victim'scomputer may be programmed to probe the network for additional victims,and install itself on their computers. Victims on the same local networkas the first victim computer may be particularly vulnerable, becausethey may exist behind any corporate firewall or intrusion detectionsystem designed to protect against Bots or malware. This is because manylocal computers are often addressed privately and may not be visibleoutside the corporate firewall, but can be readily accessed by otherlocal computers. Also, local computers may erroneously assume thatcommunications from other local computers are benign. Thus, once onelocal computer is infected, the number of infected computers mayincrease significantly.

Bots that have been installed on victim computers may maintaincommunication with what is known in the art as a Command and Controlfacility (“C&C”) operated by the malicious entity. A collection of suchBots is known in the art as a “Botnet” and has the potential to causewidespread damage, which may not even be evident to victim computersystems. Click fraud is an example that can go unseen initially. If alarge Botnet were programmed to cause widespread click fraud, it couldpotentially generate a significant number of clicks from a diverse setof fraudulent buyers, causing substantial adverse economic impact. Alarge Botnet could also be used to cause a large amount of spurioustraffic to overwhelm and shut down a targeted website. This is known inthe art as a “distributed denial-of-service attack.”

Besides trying to keep Bots out of a local network, conventionalsecurity systems also focus on trying to detect the presence of Bots oninfected computers within the local network. One way to do this is toanalyze the behavior of a known-infected computer, and generate a“signature” according to a “schema” to summarize the behavior of theBot. A schema is a multi-element template for summary information, and asignature is a schema that is populated with a particular set of values.A detailed example is given later. Typically such a schema and signaturewould be created by the security company that is protecting the localnetwork, distributed to customers, and then used by anti-virus,anti-malware software installed on each computer in the customer'snetwork to fight off known Bots. However, the usefulness of thisapproach is limited, because the ability for any anti-malware oranti-virus software operating on any single local computer to ascertainthe number of details in and the sophistication of the schema andsignature is limited by what can be observed. Also, this approach istypically not effective against attacks early in the lifetime of a newBot, known in the art as “Zero-Day Attacks”, because developers of theanti-malware and anti-virus software do not have the opportunity or timeto create a corresponding schema and signature for a new Bot.

Honeypots are known in the art as counter deceptive decoy systems thatmay be deployed along with production systems to distract attackers suchas Bots from particular targets, luring attacker/hackers away in orderto observe and learn the malicious behavior in a controlled environmentas well as to trap the attackers.

A Honeypot appears to an attacker to be a legitimate, active componentof the network containing information or resources that would bevaluable to attackers, but is actually isolated and monitored. The ideais similar to the police baiting a criminal and then doing undercoversurveillance.

So-called research Honeypots can capture a lot of information aboutspecific, known threats, but are complex and expensive to deploy andmaintain, and are therefore used primarily by research, military, orgovernment organizations. In a production network, it is simpler andmore economical to deploy a low-interaction Honeypot, but such aHoneypot typically can collect much less information about an attack andits lifecycle, and may be ineffective at identifying and characterizingZero-Day Attacks. A production Honeypot, even with high interaction, maybe designed more to waste the attacker's time that to analyze andcharacterize its behavior and share the detailed characterization with alarger community.

As will be seen, the systems and methods described herein addressshortcomings such as these in an elegant manner, by providing a highlystructured, distributed, and extensible means for constructing verydetailed characterizations of attack behaviors and for sharing suchcharacterizations within a local network and beyond

BRIEF DESCRIPTION OF THE FIGURES

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through use of theaccompanying drawings, in which:

FIG. 1 is a schematic block diagram of a network environment forperforming methods in accordance with an embodiment of the presentinvention;

FIG. 2 is a schematic block diagram showing components for implementingmethods in accordance with an embodiment of the present invention;

FIG. 3 is a schematic block diagram showing integration of componentswith a corporate network in accordance with an embodiment of the presentinvention;

FIG. 4 is a schematic block diagram illustrating virtual machines andother components implemented in accordance with an embodiment of thepresent invention;

FIGS. 5A through 5C are process flow diagrams methods for responding tobots in accordance with an embodiment of the present invention;

FIGS. 6A and 6B illustrate an example schema generated in accordancewith an embodiment of the present invention;

FIGS. 7A and 7B are schematic block diagrams of example environments forimplementing methods in accordance with an embodiment of the presentinvention;

FIG. 8 is a schematic block diagram of components for consuming IPaddress dark space in accordance with an embodiment of the presentinvention;

FIG. 9 is process flow diagram of a method for consuming IP address darkspace in accordance with an embodiment of the present invention;

FIG. 10 is a schematic block diagram of components for consumingapplication dark space in accordance with an embodiment of the presentinvention;

FIG. 11 is a process flow diagram of a method for consuming applicationdark space in accordance with an embodiment of the present invention;

FIG. 12 is a schematic block diagram of components for consuming userdark space in accordance with an embodiment of the present invention;

FIG. 13 is a process flow diagram of a method for consuming user darkspace in accordance with an embodiment of the present invention; and

FIG. 14 is a schematic block diagram of a computer system suitable forimplementing methods in accordance with embodiments of the presentinvention.

DETAILED DESCRIPTION

It will be readily understood that the components of the invention, asgenerally described and illustrated in the Figures herein, could bearranged and designed in a wide variety of different configurations.Thus, the following more detailed description of the embodiments of theinvention, as represented in the Figures, is not intended to limit thescope of the invention, as claimed, but is merely representative ofcertain examples of presently contemplated embodiments in accordancewith the invention. The presently described embodiments will be bestunderstood by reference to the drawings, wherein like parts aredesignated by like numerals throughout.

Embodiments in accordance with the invention may be embodied as anapparatus, method, or computer program product. Accordingly, theinvention may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” Furthermore, the invention may take the form of a computerprogram product embodied in any tangible medium of expression havingcomputer-usable program code embodied in the medium.

Any combination of one or more computer-usable or computer-readablemedia may be utilized. For example, a computer-readable medium mayinclude one or more of a portable computer diskette, a hard disk, arandom access memory (RAM) device, a read-only memory (ROM) device, anerasable programmable read-only memory (EPROM or Flash memory) device, aportable compact disc read-only memory (CDROM), an optical storagedevice, and a magnetic storage device. In selected embodiments, acomputer-readable medium may comprise any non-transitory medium that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

Computer program code for carrying out operations of the invention maybe written in any combination of one or more programming languages,including an object-oriented programming language such as Java,Smalltalk, C++, or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages, and may also use descriptive or markup languages such asHTML, XML, JSON, and the like. The program code may execute entirely ona computer system as a stand-alone software package, on a stand-alonehardware unit, partly on a remote computer spaced some distance from thecomputer, or entirely on a remote computer or server. In the latterscenario, the remote computer may be connected to the computer throughany type of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

The invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions or code. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in anon-transitory computer-readable medium that can direct a computer orother programmable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

Conventional honeypots have limitations and shortcomings in areasrelated to methods of data collection, engagement, detection, supportingmultiple operating systems (OSes), services and applications, scaling,number of subnets and IP addresses watched, tapping information from thecloud as well from other sources, correlating multi-dimensional events,identifying Bots, generating incident reports, and are not generallydesigned to integrate with other existing security solutions in thecloud

The systems and methods disclosed herein provide an improvedbot-detection system that addresses the foregoing limitations ofconventional approaches. In one embodiment, virtualization is used tohost multiple guest operating systems (GuestOSes) implementing honeypotsthat provide various types of network services and applications foraddressing Bots, logging insider bad behavior, and performing advancedpersistent threat (APT) detection.

In one example, a bot-detection system architecture is configured toscale in terms of the number of subnets and total number of IP addressessupported. In another example, a Bot-detection system can be deployed inan enterprise, perimeter, DMZ (referred to in the art metaphorically asthe demilitarized zone, because it is meant to isolate the corporatenetwork from potential outside attackers) and/or cloud. In oneembodiment, the Bot-detection system architecture may be configured in asingle network appliance, referred to herein as the Botsink.

One embodiment is configured to perform novel identification andanalysis of Bots and characterizing specific Bot behaviors in real time,allowing Bots to be detected and characterized quickly and accurately.This allows anti-Bot countermeasures to be put in place quickly andeffectively. In yet another embodiment, a Bot-detection system mayquickly share learned Bot characteristics among a community ofinterested or affected network sites. This would improve Botcharacterizations and would further allow for installations ofcountermeasures before more Bot attacks occur.

FIG. 1 illustrates one example of a novel Bot-detection approachaccording to one embodiment of the invention. A computer network 110 isconnected to the Internet 160. The network 110 may be owned and operatedprivately by a corporation, or may alternatively be owned and operatedby government, military, educational, non-profit, or other types ofentities. The network will be referred to as a corporate network 110 forsimplification of discussion, and those skilled in the art willunderstand that “corporate” may be substituted with other entity typeswithin the spirit and scope of these descriptions. The corporate networkis drawn as a cloud, and particular devices are shown with connectionsto the cloud, and these connections represent various hardware andsoftware configurations known in the art for communicating amongdevices. A number of devices including routers, switches, firewalls,security appliances, and other devices may be connected at the interfacebetween the Internet 160 and the corporate network 110. In someconfigurations, this collection of devices 135 is sometimes referred tometaphorically as the “DMZ”, where it is meant to isolate the corporatenetwork from potential outside attackers. Additional network devices mayexist inside the corporate network, but not included in thisillustration to avoid obfuscation of the drawing and relateddescription.

Bots 125 may be present in the corporate network 110 as well as in theInternet 160. A command and control (C&C) facility 130 operated by theoriginator of the Bots 125 may also be connected to the Internet 160 andcommunicate with Bots 125 using the Internet 160, through the corporatenetwork 110, and/or using more sophisticated means intended to hide itswhereabouts.

The detailed interconnections of devices with each other and withrouters, switches, and the like within the corporate network 110 may bemade in a variety of ways. For example, routers such as router 140 mayfurther partition the network into multiple subnets 145 for management,performance, resource allocation, and other purposes. End-devicesconnect to the subnets 145 and may include servers 170 and workstations175. A management station or server 150 may be used by networkadministrators to observe and control the network 110.

In one example, the corporate network 110 may be a local area network(LAN), where its elements are often located at a single geographic site.The Internet 160 is drawn as a cloud, and may be a Wide Area Network(WAN), where it connects geographically dispersed sites.

While elements of a corporate network 110 may be co-located at a singlegeographic site, they also may be located at multiple sites andconnected to each other with private links. In the latter case, theoverall network may still be represented as a single “corporate network”cloud 110. If desired, the various examples described herein may be usedin such a network to protect against internal threats. This may be donein one example by treating certain internal networks, devices, andservices with the same circumspection that is applied to the publicInternet in other examples described herein. To avoid obfuscation, theexamples described herein will assume that all threats are eitherconnected to the corporate network 110 via the public Internet 160 orlocated within the local corporate network 110 as shown.

The Bot-detection system 100 may have various configurations dependingon particular applications. In one example, a server device called theBotMagnet 180 is attached to one or more subnets 145. A plurality ofsubnets 145 may be connected to the BotMagnet 180 using one physicalinterface per subnet, or by combining the subnets onto a smaller numberof physical links. In one operational example, the BotMagnet may lureand/or engage with Bots 125. In another example, the BotMagnet may allowBots to infect it, and may also collect data about the Bots' behaviorand characteristics.

The BotMagnet 180 may share collected behavioral or character data witha Multi-Dimension Correlation Engine (MDCE) 185. The MDCE may record andcorrelate information about the behavior of one or more Bots 125, suchas for example multiple instances of the same Bot, and may build a newor augment an existing schema and signature that summarizes the Bots'behaviors and characteristics, as described later in the “Schemas andSignatures” section.

In one example, a Bot 125 may gather local corporate data, and may inturn cause such data to be sent back to other Bots 125, to the C&Cfacility 130, or elsewhere. The BotMagnet 180 may block such potentiallyharmful “leaks” of private corporate data, and instead gather it in adevice called the Sinkhole 190. Software in the Sinkhole 190 can analyzethe characteristics of such data to further enhance Bot detection. Itcan also optionally substitute innocuous data for the private data inorder to prolong Bot engagement without harm. The Bot-detection system100 may further include a management station or server 195 used bynetwork administrators to observe and control the operation of thesystem. Secure methods are used, as appropriate, for communication amongelements of the Bot-detection system 100. The attributes and manner ofoperation of the components illustrated in FIG. 1 are described ingreater detail below.

Scaling the Bot-Detection System

Referring again to FIG. 1, a Bot-detection system 100 may be attachedto, e.g. in data communication with, a number of subnets in a corporatenetwork 110. The Bot-detection system need not connect to all of thesubnets in the corporate network, but the system's Bot-detectioneffectiveness may be improved by connecting to as many subnets aspossible. In a large network, it may be desirable or necessary to deploya larger Bot-detection system in disparate locations. Reasons to deploya larger system include performance (a corporate network may receive toomuch attack traffic for a small system to handle), co-location (networkmay extend over a corporate campus or multiple geographical sites), andease of management (physically located with different equipment clustersor managed by different departments). For example, if the network 110has several internal routers 140, each of which partitions the networkinto subnets 145, then it may be desirable to deploy multiple BotMagnets180, with each one handling all or a subset of the subnets 145 createdby one internal router 140.

The MDCE 185 may or may not be replicated in a larger Bot-detectionsystem. In one embodiment, a separate MDCE 185 may be associated withand receive Bot behavioral information from each BotMagnet 180.

However, Bot detection is enhanced if the MDCE can collect and correlateBot behavioral information from as many sources (BotMagnets 180) aspossible, thereby increasing the generality and accuracy ofBot-detection schemas/signatures. Thus, in another embodiment, a singleMDCE may collect such information from all the BotMagnets.

In yet another embodiment, Bot behavioral information may be collectedand correlated in a hierarchical way, as shown in FIG. 2. Eachfirst-level MDCE 185 may collect and correlate Bot behavioralinformation from one BotMagnet 180, or a small number of BotMagnets 180.A second-level MDCE 187 may then collect and further correlate summariesof Bot behavioral information from the first-level MDCEs 185.

The hierarchy may be further extended. In particular, it is possible foran MDCE 185 or 187 to communicate through the Internet 160 with otherMDCEs serving other corporate networks 110, for the beneficial purposeof sharing information about new Bot attacks. Such sharing could be donestrictly hierarchically, with a “master, top-level” MDCE 188 existingsomewhere in the cloud or within a corporate network 110 and maintaininga master database of Bot behavioral information. In some embodiments,advantageously, multiple MDCEs 185 or 187 may have a peer-to-peerrelationship, much like the one that exists among other internet devicesfor distributing routes, providing domain-name services, and thelike—continuously updating each other with respect to content generatedaccording to the methods described herein by using methods understood byone skilled in the art.

Referring again to FIG. 1 and to scaling of the Bot-detection system100, the Sinkhole 190 may also be replicated, with each Sinkhole 190serving one or a small number of BotMagnets 180. The degree ofreplication may be dictated by performance requirements, co-locationconvenience, and other factors.

The Management Station or Server 195 may be replicated. Within acorporate network 110, it is typically desirable to centralize networkmanagement. This would suggest managing as many Bot-detection systems100 with a common Management Station or Server 195, or even integratingsystem 100 management with an existing, overall Network ManagementStation or Server 150. But for performance, organizational, or otherreasons, it may be desirable to provide a separate Management Station orServer 195 for each Bot-detection system 100, as will be understood byone skilled in the art. As will be understood, this is possible and ahierarchical approach again may be employed.

Virtual Machines (VMs)

A Virtual Machine (VM) is known in the art as an implementation of acomputer that operates like a single, standalone computer, but in factis one of a plurality of such implementations running on a singlehardware platform. Historically, the first VMs were pure softwareimplementations; recent years have seen the development of both hardwareand software to support easy and efficient deployment of VMs on thelatest generations of microprocessors. VMs may be deployed onmicroprocessors containing a single hardware processor (CPU), as well ason microprocessors containing multiple processors.

A collection of VMs operating on a single microprocessor may be createdand controlled by a low-level operating system called a hypervisor. EachVM is in turn controlled by a traditional operating system (OS), whichis typically unaware that it is running in a VM rather than on a single,standalone computer. Different VMs on a single microprocessor may rundifferent OSes, and different applications may run on each. VMscontrolled by a single microprocessor are typically isolated from eachother and cannot communicate with each other using traditional memorysharing and other techniques. Rather, they must communicate with eachother through a “network.” However, they need not actually communicateover the physical network. Rather, the hypervisor can create simulatednetworks or “bridges” through which they can communicate, with thehypervisor arranging internally to transfer data from one VM to another.

In one embodiment, a BotMagnet 180 may use a VM to host a GuestOS thatappears to be a real server 170 or workstation 175, insofar as otherdevices in the network are concerned. Using multiple VMs, the BotMagnet180 can take on the appearance of being multiple servers 170 andworkstations 175 at different IP addresses running multiple applicationsand services for the purpose of luring Bots 125, detecting them, andanalyzing their behavior. Further, the BotMagnet 180 may use one or moreadditional VMs to host its own protected software for overall BotMagnetcontrol and Bot detection and analysis.

The Sinkhole 190 may also include or be embodied by one or more VMs eachprogrammed to receive and analyze the outgoing traffic from GuestOSsthat are engaged with Bots 125 within a BotMagnet 180. For example, thesinkhole 190 may implement one VM for each GuestOS that is engaged witha Bot.

Basic Bot-Detection System Operation

The BotMagnet 180 may have one or more interfaces for communicating withsubnets 145 in the corporate network 110. The network administrator orsome other network management module (e.g. a dynamic host configurationprotocol (DHCP) module) configures the BotMagnet 180 with one or moreotherwise unused IP addresses from the subnets 145, e.g. assigns an IPaddress to the BotMagnet by which packets may be addressed to theBotMagnet 180. The network administrator may use the ManagementStation/Server 195 to perform such configuration. The BotMagnet 180 thenmay create a GuestOS VM corresponding to each such IP address. Thus,each GuestOS VM may have its own IP address, and through the Hypervisormay also be assigned its own unique MAC address for layer-2 networkconnectivity. Thus, each GuestOS VM, for all outward appearances, maybehave like an independent physical computer communicating at itsassigned IP address. Each GuestOS VM is an instance of an operatingsystem, which may be a different OS or version thereof on different VMs.Each GuestOS VM is also loaded with a set of applications, such as webapplications and services, which again could be different on differentVMs. OSes, applications, and services may be configured either by thenetwork administrator or automatically by the Management Station/Server195 to ensure that the BotMagnet is hosting an appropriate mix ofpotentially vulnerable software.

Applications and services existing on a GuestOS VM (or on any server orworkstation, for that matter) are accessed by potential clients whenclients access them through the network interface. A typical applicationor service may be accessed using a well known protocol such as TCP orUDP and a “port number” such as SMTP (25), HTTP (80), RLOGIN (513), FTP(20-21), or one of many others. If a computer does not offer aparticular application or service, it may discard incoming trafficdirected to the corresponding port. Otherwise, it directs such trafficto the appropriate application or service program. Thus, a GuestOS mayaccept only inbound traffic corresponding to the applications andservices that have been configured on it.

Bots 125 and other malicious entities perform “port scans” on targetnetworks in order to find available applications and services, and thenengage with them with the goal of finding vulnerabilities that can beexploited to gain further access to the target. A port scan typicallyattempts communication with all of the IP addresses that might be usedin the target network, and for each IP address it attempts all of theport numbers for which it may be able to find a vulnerability.

Thus, if a large proportion of a network's IP addresses are assigned tothe Bot-detection system 100, and a large number of applications andservices are offered there, there is a high probability that a Bot'sport scan will soon encounter a GuestOS VM in the Bot-detection systemwhere its behavior will be recorded and subsequently analyzed.

The Bot-detection system 100 is designed to attract Bots 125 and allowthem to infect GuestOS VMs, so that behavioral details of Bot operationcan be recorded and subsequently analyzed. The GuestOSes in theBotMagnet 180 may have no special mechanisms to prevent Bot infections.Indeed, Bot infections are desired.

Consider a Bot 125 that is able to communicate with a GuestOS VM throughthe FTP port. It may try to download an executable file such as a copyof itself into the GuestOS file system, and subsequently try to executeit. If these operations would have been allowed by the GuestOS,applications, and services running on a real computer, they will beallowed on the GuestOS VM. The GuestOS VM therefore becomes infected.

Bot operations on a GuestOS VM may advantageously be contained such thatthey cannot actually harm the corporate network 110 and the devicesattached to it. Because of the Bot's containment in a VM, it can beprevented from doing any direct harm. To understand how this is done insome embodiments, the concepts of “inbound” and “outbound” traffic on aVM should first be understood. Inbound traffic is traffic from anexternal entity that results in the VM taking internal actions, such asallowing the entity to log in or run a service or program, or acceptingdata that is sent to it, such as storing a file that has been downloadedby the external entity. Outbound traffic is traffic in which the VMsends potentially private data to an external entity. For example, a webpage that is normally visible to all external entities is not consideredprivate, while an arbitrarily selected file may be consideredpotentially private. A basic principle of operation for a GuestOS VM isthat it may allow and act upon all inbound traffic from externalentities, while it may block all outbound traffic directed to externalentities.

For example, suppose the Bot 125 now running within the infected GuestOSVM tries initiate its own port scan of the corporate network, or triesto transfer a file back to its C&C facility 130 using FTP. The BotMagnet180 may be programmed such that no outbound traffic can be sent from aGuestOS VM to the corresponding connected subnet 145. Thus, theconsequences of the infection are effectively blocked, no matter how badthings may look inside the infected GuestOS VM.

On the other hand, it may not be possible to fully record and analyzethe behavior of a Bot 125 unless it is allowed to continue itsengagement in a meaningful way. The Sinkhole 190 is the system componentthat makes this possible in some embodiments. For selected inboundtraffic, the BotMagnet 180 may be configured to forward such traffic tothe Sinkhole 190, which may contain one or more VMs corresponding toeach GuestOS VM of the BotMagnet 180 with which it is currently engaged.Each Sinkhole VM may further configured with the applications andservices that it is expected to handle.

For example, if a Sinkhole VM is expected to handle HTTP traffic, thenit could be configured with an Apache Web Server. Outbound traffic fromthe Apache Web Server may then be sent back to the requester (such as aBot 125 elsewhere in the corporate network 110 or Internet 160). The webpages and other information visible through this server would beconfigured in much the same way as in a conventional honeypot, in orderto lure a Bot 125 to engage further without disclosing anything ofvalue. This provides an opportunity to record and analyze the furtherbehavior of the Bot 125 on an infected target.

The MDCE 185 receives extensive log information from both the GuestOSVMs and the Sinkhole VMs, as well as certain information gleaned by theBotMagnet's control software (which manages the VMs). Thus, the MDCE isable to track the engagement and profile the entire lifecycle of a Bot125. Once the Bot's behavior has been profiled in a schema/signature,the MDCE 185 may create an alert notifying the network administrator,and optionally may distribute the schema/signature to potential victimservers 170 and workstations 175 in the network. As is understood in theart, after receiving an alert, the network administrator may take stepsto prevent further infection, such as blocking the attacker at thenetwork DMZ/firewall. Also, upon receiving a schema/signature describingthe new threat, anti-virus/malware software running on a potentialvictim can take automatically take action when a matching behavior isdetected, as is understood in the art. To discover already infectedvictims, a network administrator can invoke thoroughvirus/malware-scanning operations to be run on each potential victim,and/or can use standard software tools to examine their log files forbehavior patterns that match the schema/signature, as is also understoodin the art.

The Bot-detection capabilities of the system 100 are enhanced by thesystem's ability to capture and correlate events occurring both in theGuestOS VMs when an infection begins, and in the Sinkhole VMs, as theconsequences of an infection begin to occur. However, embodiments of theinvention are possible with no Sinkhole 190 or no BotMagnet 180.

For example, operating without a Sinkhole 190, it is still quitefeasible for a GuestOS VM in the BotMagnet 180 to send log informationto the MDCE 185, which can correlate information from this and otherGuestOS VMs in order to build a profile, albeit a less extensive profilethan what could be done in a complete system. Yet such a system stillhas the advantage of creating such profiles from multiple infectedGuestOS VMs and subnets, and such profiles may also be correlated withBot information gleaned from other facilities.

Conversely, operating without a BotMagnet 180, it would still bepossible for real servers 170 and workstations 175 to be configured withsoftware that collects behavioral information such as logs and sends itto the MDCE 185 for correlation with other information as before.Further, if the real server 170 or workstation 175 is “suspicious” aboutany activity, for example based on its origin or behavior pattern, itmay forward the session to the Sinkhole 190 for engagement, in much thesame way that a BotMagnet GuestOS VM would as described above. In thiscase, the MDCE can build a more complete profile, because it cancorrelate behavioral information from both the originally targeted realserver 170 or workstation 175 and the Sinkhole 190.

In yet another example, embodiments could be combined with applicationSer. No. 14/074,532 filed Nov. 7, 2013, which is incorporated herein byreference in its entirety. The Inspector 255 in that application,instead of redirecting blocked traffic to a Labyrinth 257 or 258, couldredirect it to a GuestVM OS in the BotMagnet 180 or directly to theSinkhole 190. One or more GuestVM OSes and corresponding Sinkhole VMsmay be instantiated to handle such traffic, either by configuration ordynamically as needed. As the MDCEs 185, 187, and 188 in presentinvention are designed to share information with other MDCEs andsecurity services; they could also share information as appropriate withthe Cloud Inspection Service (CIS) 262 in application Ser. No.14/074,532.

For robust operation of the Bot Detection System 100, communicationamong the BotMagnet 180, the MDCE 185, the Sinkhole 190, and theManagement Station or Server 195 should be secure. Conventional methodsmay be used to encrypt such communication. Also, it is important toensure that the MDCE 185 and the Management Station or Server 195 cannotbecome infected, and that BotMagnet 180 and the Sinkhole 190 can becomeinfected only within the GuestOS and Sinkhole VMs as desired, and notwithin their supporting VMs and processes. This can be ensured, in part,by using secure, private communication between these elements, forexample by using secure tunnels. In the Botsink appliance, describednext, most of such communication privacy is inherent becausecommunication occurs internal to the appliance.

BotSink Appliance Architecture

In a preferred embodiment, the Bot-detection system 100 is integratedinto a single network-connected device, called the BotSink appliance. Asshown in FIG. 3, the BotSink appliance 300 connects to multiple subnets145, using one or more physical interfaces 310 as discussed previouslyin connection with FIG. 1. BotSink appliance 300 may also connectthrough an interface 320 to the corporate network 110 for the purposesof communicating with other BotSinks or standalone MDCEs 185 or 187,Management Stations or Servers 195 or 150, and for any other requiredpurpose. Secure communication is used as appropriate. The interface 320to the corporate network may or may not use the same physical link(s) asthe subnet interface(s) 310.

FIG. 4 shows major software components of the BotSink 300. It hostsmultiple GuestOSes with multiple services to lure bots and usesdistributed processing both for data/traffic collection and analysis.The appliance supports multiple interfaces, both physical and virtualinterfaces, that can be plugged into different parts of thephysical/virtual networks. The unique features and capabilities of theBotSink appliance are based on a distributed architecture comprising thefollowing components running in multiple VMs:

-   -   1. The Hypervisor 410 that provides virtualization.    -   2. GuestOS VMs 420 for loading different types and instances of        operating systems and performing some or all of:        -   a. Running various network and applications services        -   b. On some or each of the GuestOS VM the following set of            services may be run:            -   i. Log collection across various modules            -   ii. Transformation of the raw logs into well defined                formats            -   iii. Forwarding events to a centralized location handled                by Log Shipper.    -   3. Sinkhole VMs 430 to be the destination for selected traffic        originating from each of the Guest VMs. 3. The sinkhole VMs 430        perform some or all of the following:        -   a. Confining traffic with in the appliance.        -   b. Running various network and applications services for            engaging with Bots.        -   c. Event collection, transform and shipping module 415.        -   d. Proxy module for engaging with C&C and other traffic            communication for a real world interaction.    -   4. Events Collector and Storage Engine 435 may perform some or        all of the following:        -   a. This module is responsible for receiving some or all the            events from various components running on the different            GuestOS and Sinkhole VMs.        -   b. Some or all of the events are stored in a database 445            for further analysis by the Multi-Dimension Correlation            Engine.        -   c. This includes log rotation, threshold-based cleanup and            so on.    -   5. Multi-Dimension Correlation Engine (MDCE) 455 to correlate        events for Bot detection. The MDCE may perform some or all of        the following:        -   a. This is a component for Bot detection, the engine            responsible for correlating the event data and generating            meaningful results for detection of Bots. It processes            events from individual hosts and generates schemas,            signatures, and alerts by means of correlation.        -   b. Actions may be taken/driven based on the results of the            correlation. Running the correlation may be event-driven and            also may be run at regular intervals.        -   c. The Bot detection rate is high since many individual            events can be collected from the GuestOS and Sinkhole VMs.        -   d. Exchanges information with higher-level MDCEs and other            services in the corporate network and/or the Internet for            global analytics.        -   e. Taps into the cloud (Internet) for getting real-time            information or metadata about BlackList IP address, URLs,            virus signatures, social media and crowd-sourced            information, and information from security devices and other            sources.    -   6. Master Controller 460 performs some or all of:        -   a. Running in the Privileged Mode, this software has total            control over each of the GuestOS and Sinkhole VMs            instantiated.        -   b. Manages, creates and destroys VMs, bridges, and other            resources.        -   c. Monitors to ensure all the applications and services are            running as necessary.        -   d. Manages connectivity of VMs to each other and the            network, for example, prevents outbound traffic from a            GuestOS VM 420 from going out on a subnet interface 310, and            redirects it as required to a Sinkhole VM 430.    -   7. UI for configuration and reporting 450        -   a. Forwarding of alerts to other Security devices.    -   8. Additional security and monitoring services 470 may be used        by MDCE 455, UI 450, Master Controller 460, and Event Collector        435.

FIG. 4 shows two GuestOS VMs 420, each of which may run one or morenetwork services and applications such as FTP Server, Apache-based HTTPserver, SSH server and so on. Any number of GuestOS VMs may be provided,subject to performance and other resource limitations. On each of theGuestOS VMs, there may be an Event Collector and Shipper 415 thatcollects events, logs them, and forwards them to the Event Collector andStorage Engine 435. Likewise, two Sinkhole VMs are shown, but any numbermay be provided to service outbound traffic from the GuestOS VMs 420.The Sinkhole VMs 430 may likewise include one or more web services andresources as the VMs 420 and may likewise include an event collector andshipper 415.

In FIG. 4, the bottom set of software modules 440 (“Master”) may berunning in Privileged Mode and have higher privileges configured by theHypervisor 410, compared to the “Slave” GuestOS and sinkhole VMs thatrun in Unprivileged Mode. The Slave software modules may beoff-the-shelf software such as standard releases of various operatingsystems, web services, applications, and utilities such as eventloggers.

Software modules 4-8 listed above may run in a single VM, while in someembodiments they may advantageously be split among a plurality of VMs.As such, they are protected from any of the other VMs. These componentsmay run in Privileged Mode, which means they have access to theHypervisor 410 to create, destroy, and otherwise access, control andmonitor VMs, bridges, and other resources, while in some embodiments theGuestOS of VMs 420 and Sinkhole 430 VMs cannot.

Some or all outbound traffic originating from any of the GuestOS VMs 420may be either dropped or redirected to a Sinkhole VM 430, thus initiallyconfining potential outbound traffic within the appliance, e.g. system100. The Sinkhole VM may then allow selected traffic to be passed as is,modified or substituted and returned to the requester (such as a Bot125) so that engagement may proceed in a harmless manner.

Multiple strategies and methods may be used to harden the Mastersoftware modules so that they do not get infected or become targets ofan attack. Also, a GuestOS VM 420 being infected may advantageously haveno impact on any of the other system components or other VMs, in termsof the CPU usage, resources, and so on, nor on the Master softwaremodules.

Typical System Operation

FIG. 5A is a flowchart showing an example method 500 of operation of theBotMagnet 180 in a Bot Detection System 100 or BotSink appliance 300. Instep 502, GuestOS VMs in the BotMagnet 180 are created and configured tooffer various services, applications, and databases. In step 504, a Bot125 is performing a port scan using the IP (internet protocol) addressof one of the GuestOS VMs and is probing for services offered at that IPaddress. The Bot 125 may be located in the Internet 160, or it may beinside the corporate network 110, running on a server 170 or workstation175 that has been infected.

In step 506, the Bot 125 is attempting to access the service at aparticular port number. If the GuestOS VM does not offer 508 the serviceit logs the probe, but there is no engagement 508 with the Bot. Loggingthe probe is useful for automatically detecting port scans. If theservice is offered, the Bot is allowed to engage with the GuestOS VM,and the service is performed in step 510. In this step, all of thecommunication and other activity normally associated with the serviceoccurs.

A typical Bot, once engaged with a service or application on a victimsystem, looks for vulnerabilities that may allow it exploit the victimsystem, for example, by downloading, installing, and running anexecutable file. The executable file typically contains a program thatmay be able to initiate outbound traffic, and it may be a copy of theoriginal Bot 125 itself, as the Bot attempts to spread itself laterallyacross the network. Thus, a copy of the Bot 125 may be running insideone or more GuestOS VMs in the BotMagnet 180, as was shown in FIG. 1.

During the engagement, agents in and associated with the GuestOS VMcapture and log events in step 512. This step may be performedperiodically, e.g. be substantially a continuous activity, that may takeplace in parallel with the normal activity of the service beingperformed in step 510. Periodically, or on the occurrence of particularevents (such as the Bot attempting to send outbound traffic for thefirst time), in step 514 activity logs may be sent to the MDCE 185 forcorrelation with other events logged elsewhere.

During the engagement in step 510, the Bot may attempt to send varioustypes of outbound traffic. One type may be an attempt by the Bot tocontact its C&C facility 130. Another type may be an attempt to performa port scan on other servers 170 and workstations 175 in the localnetwork or beyond, and to infect any vulnerable ones that are found. Yetanother type of outbound traffic may be an attempt to send files orother sensitive information (such as passwords, security keys,configuration information, and the like) to the C&C facility 130 orelsewhere.

In step 516, an attempt to send outbound traffic from the GuestOS VM isdetected. Like step 512, step 516 is a periodic, e.g. substantiallycontinuous activity, that may take place in parallel with the normalactivity of the service being performed in step 510. Ensuring thatoutbound traffic is blocked or redirected may typically be a function ofthe Master Controller 460 software module running in Privileged Mode inthe BotMagnet 180 or the BotSink 300. In step 518, a decision is made bysuch software whether to block such traffic or to redirect it to aSinkhole VM in step 520. In either case, the activity is logged in step512 for eventual sending to the MDCE in step 514.

When sending of outbound traffic is attempted for the first time in aparticular GuestOS VM, the blocking and redirecting software may also beresponsible for arranging to allocate or instantiate an associatedSinkhole VM and install and run the appropriate services andapplications on it; in the present example this operation is performedby the Sinkhole itself, as will be seen next.

FIG. 5B is a flowchart illustrating an example method 522 of operationof the Sinkhole 190 in a Bot Detection System 100 or BotSink appliance300. In step 524, the Sinkhole 190 receives outbound traffic from aparticular GuestOS VM which has generated outbound traffic and hasdecided to send it to the Sinkhole 190 rather than drop it, for examplein step 520 of FIG. 5A. In step 526, the Sinkhole 190 determines whetherit already has a Sinkhole VM that is processing outbound traffic fromthe particular GuestOS VM and, if so, directs the traffic to thatSinkhole VM in step 528. If not, then in step 530 it either allocates apre-configured Sinkhole VM from an available pool, or instantiates a newSinkhole VM and configures it with the services and applications thatmay be needed for the new engagement. In particular, the outboundtraffic may be inspected to determine a service or applicationreferenced by the outbound traffic and that service or application maybe provisioned on the Sinkhole VM. Once the Sinkhole VM exists and isready to accept traffic, step 528 directs the outbound traffic to it.

In step 532, the Sinkhole VM decides whether to engage with the Bot 125.The decision whether to engage is based at least in part on the natureof the outbound traffic. If there is no engagement, then the traffic isdropped 534; otherwise it is forwarded to step 536 for engagement.

Whether or not engagement occurs, events and traffic may be captured andlogged in step 538. The logging in step 538 is a continuous activitythat takes place in parallel with the normal activity of any engagementbeing performed in step 536. Periodically, or on the occurrence ofparticular events (such as determining the name or address of the Bot'sC&C facility 130 for the first time), activity logs may be sent to theMDCE 185 for correlation with other events logged elsewhere, e.g. eventslogged by a GuestOS for the Bot the same Bot that generated the trafficbeing processed by the Sinkhole VM according to the method 522.

Any kind of engagement may occur in step 536, if the Sinkhole VM isconfigured with the appropriate services and other software. Forexample, if the outbound traffic uses the HTTP protocol, the Sinkhole VMmay host an Apache web server (e.g. provisioned on-the-fly to host anApache web server) to respond to the Bot's web-page requests and serveup pages that may trap the Bot into continuing the engagement, givingthe Bot detection system 100 more opportunities to learn about and logthe Bot's behaviors and what it is ultimately looking for.

In another example, the outbound traffic may be a port scan that hasbeen initiated by the Bot 125 in the GuestOS VM. In this case, thesoftware in the Sinkhole 190 may ensure that all port scans are directedto one or more Sinkhole VMs, e.g. one or more other Sinkhole VMs, thatoffer various services and applications. Thus, the Bot 125 in the localGuestOS VM may be tricked into engaging with a service running on aSinkhole VM. This provides more opportunities for the Bot DetectionSystem 100 to observe and log the behavior of the Bot 125, such asaccording to the methods described herein.

In another example, if the outbound traffic uses the IRC (Internet RelayChat) protocol, then it is likely to be an attempt by the Bot tocommunicate with its C&C facility 130. In this case, software in theSinkhole VM may engage with the Bot using the IRC protocol and attemptto learn valuable information about the Bot. For example, it may be ableto learn the URL (uniform resource locator) of C&C facility, or theidentity of the Bot. If the outbound traffic includes a DNS request tolearn the IP address associated with the C&C's URL, a DNS service in theSinkhole VM may respond with the IP address of the Sinkhole VM itself,thereby fooling the Bot into communicating directly with the Sinkhole VMas if it were the C&C facility, further enhancing the Sinkhole VM'sopportunity to learn and log more details of Bot-C&C interaction.

In yet another example, the Bot may be attempting to send in theoutbound traffic corporate data that it accessed in the GuestOS VM. Insuch a case, the Sinkhole VM may simply maintain the HTTP, IRC, FTP, orother communication and data-transfer channel, and log the data thatcomes across it for further analysis, e.g. report the data to the MDCE185 as described above.

In the examples above, the Sinkhole VM continues to prevent the originaloutbound traffic received from the GuestOS VM from leaving the confinesof the Sinkhole 190. However, the Sinkhole VM may be configured tooptionally enable a feature called Proxy Mode. When the Sinkhole VMattempts to send outbound traffic as a result of the engagement in step536, step 540 determines whether Proxy Mode is enabled. If not, then thetraffic is blocked in step 542 and logged in step 538. If Proxy Mode isenabled, the Sinkhole VM, with the cooperation of the Master Controller460 software, may in step 544 allow the original outbound trafficreceived from the GuestOS VM to exit the Sinkhole 190. The Proxy Modesoftware may also modify the source IP address and other information inthe outbound traffic so that further engagement occurs directly with theengaging software running on the Sinkhole VM, rather than with theGuestOS VM.

Proxy Mode may be especially useful if outbound traffic is determined tobe an attempt by the Bot 125 to communicate with its C&C facility 130.Such traffic activity is monitored in step 538 along with otheractivities of the Bot. Thus, Proxy Mode may be particularly useful for“Bot research.” That is, if the Bot Detection System 100 discovers a Botwhose behavior does not match any previously known Bot, networkadministrators or others may wish to investigate the Bot further todetermine what additional exploits it may be capable of, and what kindof information or resource theft it is ultimately seeking By enablingProxy Mode in a carefully controlled environment, the networkadministrators create the opportunity for Bot communication with the C&Cfacility 130, so that more information on the Bot and the C&C facilitymay be revealed. In Proxy Mode, it is also possible for the researchersto modify the outbound traffic to the C&C facility to reveal even moreinformation. As for the method 5A, logs of events captured at step 538may be sent 546 to the MDCE for processing according to thefunctionality ascribed to the MDCE 455 herein.

FIG. 5C is a flowchart illustrating an example method 548 of operationof the MDCE 185 in a Bot Detection System 100 or the MDCE 455 softwaremodule in a BotSink appliance 300. In step 550, the MDCE collects eventsand log information from one or more BotMagnets 180. Such informationtypically may be collected and consolidated from multiple GuestOS VMsand other software on each BotMagnet 180 by a software module such asthe Event Collector and Shipper 435 that was described previously inconnection with the Botsink 300.

Similarly, in step 552 the MDCE collects events and log information fromthe Sinkhole VMs and other software running in one or more Sinkholes190. In step 554, the MDCE collects schemas/signatures and otherinformation from other MDCEs 185, 187, and/or 188. In step 556, the MDCEcollects schemas/signatures and other information from otherBot-information sources. Such sources may include publicly accessibleservices that collect and publish information on known Bots usingsoftware such as Snort and formats such as the STIX language to describeIOCs (Indicators of Compromise) and Bot signatures. Such sources mayalso include privately accessible services with which the operators ofthe Bot Detection System 100 have cooperation agreements.

In each case above, the events collected are placed into a databasewhere they can be accessed by further steps. In step 558, the MDCEcorrelates information received from the various sources, to build andenhance Bot schemas/signatures. In particular, it correlates informationfrom each particular GuestOS VM and the associated Sinkhole VM, if any,and determines which information may indicate the presence of a Bot andshould be included in a corresponding schema/signature.

In step 560, the MDCE compares a new schema/signature with otherschema/signatures in its database and determines whether it maycorrespond to a new Bot, e.g. a new type of Bot. The otherschema/signatures may have been created as a result of other activity inthe same Bot Detection System 100, or they may have been received fromother MDCEs in step 554 or other sources in step 556.

If step 560 determines that the new schema/signature corresponds to anexisting Bot, in step 562 the MDCE may combine the new schema/signaturewith the existing schema/signature(s) for the same Bot to create anenhanced signature, and update its database accordingly. In step 564,the MDCE may share the enhanced signature, if any, with other MDCEs andpublicly and privately accessible Bot-information services.

If step 560 determines that the new schema/signature does not correspondto an existing Bot, in step 566 the MDCE may update its database withthe new schema/signature and continue to step 564 to share the newsignature with others. It may continue to step 564 immediately or,depending on MDCE configuration or characteristics of the Bot such aspotential for damage, it may elect to wait until more activity or moreinstances of the Bot have been detected.

In step 564, the MDCE may share a new or enhanced schema/signature withSinkholes 190. Having the ability to access the signatures of both newand previously known Bots may provide useful capabilities in Sinkholes190. For example, a Sinkhole VM may decide whether or not to enableProxy Mode or alert a research team depending on whether a Bot that itis engaged with is new or is already well known.

In step 564, the MDCE may also share a new or enhanced schema/signaturewith servers 170 and workstations 175 that are capable of interpretingsuch a signature and using it to block any attacks that should bedirected at them. For any of the sharing partners above, the MDCE mayshare some or all of its schema/signature database with othersperiodically or upon other events, triggers, or requests, not just uponthe creation of a new or enhanced schema/signature. In step 568, theMDCE may send alerts to a network administrator and/or others,indicating that a new Bot or an instance of a known one has beendetected. If desired, such alerts may be sent earlier in the process,based on configuration or other characteristics of the detectedactivity, such as the potential for damage.

Schemas and Signatures

As introduced previously, a schema is a multi-element template forsummarizing information, and a signature is a schema that is populatedwith a particular set of values. A schema may have just one or a fewelements. However, an aspect of the invention is to base Bot detectionnot just on one or a few individual events like network behavior orsignature but across multiple dimensions across various VMs, services,and applications across multiple subnets. Thus, the schema fordescribing a particular Bot may have many elements corresponding to themany dimensions, and the values that populate the elements may capturethe behaviors of many instances of the Bot. The populated schema may becalled a “Multi-Dimension Bot Lifecycle Signature.”

These multiple dimensions can be broadly categorized into, but notlimited to, the following:

-   -   1. Network activity        -   a. Transmit packets        -   b. Receive packets    -   2. Connection tracking        -   a. Inbound        -   b. Outbound    -   3. Probes/scans        -   a. ARP request/ARP response        -   b. TCP SYN, TCP Reset, ICMP redirects and so on    -   4. Network behavior        -   a. Time of activity        -   b. Burstiness        -   c. Amount of data transferred    -   5. OS related activity        -   a. OS system calls        -   b. Call stack        -   c. Delay or sleep    -   6. System activity        -   a. Registry key changes        -   b. Installation of other programs        -   c. File drops        -   d. Directory creation    -   7. Application activity        -   a. Authentication (involves audit logs)        -   b. Usage of resources    -   8. Application-related backend activity        -   a. Database access        -   b. Invoking other utilities and programs    -   9. Log activity        -   a. Log file        -   b. Utilities like Firewall, iptables, other security            programs, antivirus, and so on        -   c. Events detected and reported by other security programs        -   d. Snort (intrusion detection and prevention system)        -   e. Generate new signature both for C&C as well for the            traffic generated by the Bots. These signatures can be            exported and shared among security devices.

Such activities may be captured on either of the GuestOS VMs andSinkhole VMs on which they occur or by which they are detected. In somecases, the capturing is accomplished by small agents that are installedwith the GuestOS or Sinkhole software, typically monitoring calls to theOS kernel for various services. How to create and install such agents isunderstood by those skilled in the art. For example, among other thingsthe Linux Audit System has the ability to watch file accesses andmonitor system calls(c.opensuse.org/products/draft/SLES/SLES-security_sd_draft/cha.audit.comp.html,Chapter 30, Understanding Linux Audit).

In general, monitored activities may include any of the following:

-   -   1. file access    -   2. file modification    -   3. file transfers (incoming or outgoing)    -   4. directory creation/destruction    -   5. registry queries & modifications    -   6. new-process creation    -   7. process destruction    -   8. input/output, including use of cameras and other peripherals    -   9. keystroke and mouse capture/logging    -   10. display activity    -   11. installation or removal of agents

An example of a schema written in XML is shown in FIGS. 6A and 6B. Forthe purposes of illustration, this schema has been limited to a fewdimensions and has correspondingly few elements. However, the number ofand complexity of the elements may be expanded to describe any desirednumber of Bot lifecycle behavioral dimensions. The elements of theexample schema are described in the paragraphs that follow.

The first nine lines of the example schema in FIGS. 6A and B containidentifying information about the schema itself, such as the name,description, creation date, and author of the schema. The definition ofthe schema begins at line 10.

On line 11, the “OR” operator specifies that matching any of theelements within its scope creates a match of the schema. Otheroperations such as “AND” can be used, and logical conditions can benested as desired. The “id” and its value are for identification andtracking purposes and are placed in the schema by its author, the MDCEin the present example.

The first element within the “OR” operator's scope is specified on lines12-15. This element matches a file whose name matches the string value“fsmgmtio32.msc”, which in this schema is the name of a file that mayhave been accessed or installed by a Bot.

The next element is specified on lines 16-19, and matches a file whoseMD5 checksum equals a specified value. Thus, if the Bot installs thesame malicious file in different victims, it will still be matched evenif a different filename is used. Or, additional elements could be addedto the schema to specify additional variations of filename or MD5checksum in different instances of the Bot.

The next two elements, on lines 20-23 and 24-27, match a DNS lookup foreither of two URLs that may correspond to a C&C facility for the Bot.The element on lines 28-31 matches a particular remote IP address thatmay be associated with the Bot.

The example schema's list of elements continues in this manner, witheach element specifying a value to be matched. The element on lines60-68 is worth pointing out, as it matches an event detected by SNORTsoftware running in the Master, privileged layer of software in aBotMagnet 180, Sinkhole 190, or BotSink 300. The element on lines 69-86is also worth mentioning, as it is “composite” element involving severalvalues and two logical operations, designed to match a Microsoft Windowsregistry entry. The registry-item path must match“Software\Microsoft\Windows\CurrentVersion\Run”, AND the registry-itemvalue must match “\WindowsNT\svchost.exe” OR “\WindowsNT\svclogon.exe”.

The event and the value to be matched in each element may have beenlogged originally by a GuestOS VM 420, a Sinkhole VM 430, or in somecases by other software modules running on the BotMagnet 180, Sinkhole190, or BotSink 300. In any case, it is the responsibility of the MDCE185, 187, or 188 to determine which events may be relevant to aparticular Bot and to incorporate appropriate matching elements as itbuilds or augments the corresponding schema and the values that arematched, thus creating a multi-dimension lifecycle signature for theBot.

Multi-Dimension Correlation Engine Details

As previously explained, one component of the Bot-Detection System 100is the multi-dimension Correlation Engine (MDCE) 185, 455. One functionof the MDCE may be to correlate multi-dimension individual eventscollected across various modules across different VMs to generate amulti-dimension schema and signature corresponding to a Bot 125. Thatis, the MDCE observes Bot behavior and thereby generates a “BotLifecycle Signature” using a schema. The MDCE 185, 455 can importvarious signatures/schemas from other MDCEs 185, 187, and 188 and fromthe cloud, as well as transform these schemas for export in variousstandard formats. The MDCE can reduce false positives by dynamiclearning and incorporating other information like white lists and so on.

The MDCE can classify as well as group the events according to the typeof Bot infection phases such as those described in the section onLifecycle of Bot Detection.

The MDCE supports importing of data related to one or more Bots frommultiple sources and formats as well feed this data to the MDCE,resulting in better detection. Similarly, Bot related data likesignatures, traffic, events, pcap (packet capture) and so on can betransformed into various formats for exporting to other systems. Some ofthe input/output formats supported are listed below:

-   -   1. Open Framework for Sharing Threat Intelligence (OpenIOC)        format    -   2. Structured Threat Information eXpression (STIX) format    -   3. SNORT rules/signatures    -   4. other industry-standard formats that may exist or be        developed    -   5. customized and proprietary formats

Actions Taken on Bot Detection

On detection of any infection on any of the Guest OS VMs 420 based onthe collection of data and events, the Master Controller 460 softwaremodule running in Privileged Mode in the BotMagnet 180 is responsiblefor taking a set of actions on that particular VM without any userinvolvement. The list of possible actions includes:

-   -   1. Stop the service    -   2. Cleanup by running different Antivirus utilities    -   3. Destroy the VM    -   4. Respin the VM    -   5. Quarantine the VM for further observation    -   6. Wait for a predefined timeout value, or as configured by        user, and then respin the VM.

In any of these cases any outbound traffic from the infected GuestOS VM420 may always be dropped by the GuestOS VM or it may be redirected to aSinkHole VM 430 which may send it, may modify and then send it, or maydrop it, as was explained previously in connection with FIG. 5B. Hencethere may advantageously be no leakage of any outbound traffic from anyof the VMs on the BotSink appliance 300.

A Bot's behavior may be similar to one that has been seen before, eitherby the local MDCE 185, by another MDCE 185, 187, or 188, or by anothersecurity service that has shared Bot signatures using a known format. Insuch a case, the MDCE that has detected the Bot may export the locallyconstructed signature to these other devices and services to enhanceglobal recognition of the Bot. If the Bot is not recognized—a so-calledDay Zero attack—the MDCE may advantageously share the locallyconstructed signature with other devices and services.

In either case, signatures shared with other MDCEs, devices and servicesmay characterize Bot behavior in much more detail because of uniquecapability of the Bot-detection system 100 to capture very detailedbehavioral information from multiple sources over the entire lifetime ofthe Bot.

Lifecycle of Bot detection

This section describes a sample Bot and gives details right from theinfection phase to the Command & Control (C&C) communication phase. Thelifecycle of a Bot infection process may be classified into five stages,called “Bot infection phases”:

-   -   1. E1—Inbound scanning—scanning a computer within the network.    -   2. E2—Exploit—when the inbound scan successfully exploits/gains        access to use a computer within the network using various        exploit attack vectors.    -   3. E3—Egg download—downloading a copy of the complete Bot to        infect and execute on the exploited computer.    -   4. E4—Outbound scanning—Infected machine within the network        scans machines inside or outside the corporate network for        vulnerabilities for infecting more systems.    -   5. E5—C&C engagement—infected machine contacting the command and        control center. (Note: this extract is adapted from        http://rise.cse.iitm.ac.in/wiki/images/9/98/Botnet_report.pdf).

The above list of phases may be extended by adding two or more phases,such as:

-   -   1. E6—Infection Phase resulting in payload drop onto a new        target.    -   2. E7—Malicious Traffic generation like generating SPAM, DDOS,        etc.

On the other hand, it is entirely possible that some Bots may skip a fewphases and may execute the phases in a different order. Also someevents, like E4 and E5, can happen independently of each other.

This section details and lists multi-dimension events, their processingand their grouping, which results in detection of the sample Bot by theBotSink system 100 or appliance 300. This also includes the subsequentgeneration of alerts and target client list reporting. The events listedhere may be specific to the Botsink system 100 or appliance 300implementation and follow a generic format used to log each of theevents, as shown below:

-   -   1. Time Stamp Field: Indicates the timestamp, such as in UTC        format, of when this event was captured    -   2. Event Name: describes the type of event or a module name        responsible for this event.    -   3. Type: subtype of the event, like request/response    -   4. Protocol: The transport protocol such as TCP, UDP etc.    -   5. Flags: Protocol specific information.    -   6. L2: MAC-layer specific information, like MAC address    -   7. L3 info: IP addresses of both source and destination, and        whether IPv4 or IPv6    -   8. L4 info: port number of source and destination, service info    -   9. Extended description: Raw or summary description related to        event

Two examples of events are:

-   -   1. <TimeStamp=1222,Event=ARP, type=request, src Ip=123.2.1.3,        mac=mm:aa:bb:cc:dd:ee:> (where ARP means Address Resolution        Protocol).    -   2. <TimeStamp=225,Event=Network, Type=TCP, sub-type=connection        established (Event 023, conn established, client IP=x.x.x.x,        destination port=yy, target ip, port etc.) (where TCP is        transmission control protocol).

Sample Trace for Bot Called BBB

A Bot installed on a workstation or server initiates a port scan therebyprobing to discover new computers for infection to laterally spreaditself. The BotSink appliance engages with the Bot by responding to allprobes that arrive at each of the GuestOS VMs that it hosts. It alsologs these probes. The following set of events are triggered:

-   -   1. <TimeStamp=1222,Event=ARP, type=request, Ip=x.x.x.x,        mac=mm:aa:bb:cc:dd:ee:>    -   2. <TimeStamp=1223,Event=ARP, type=response, ip=x.x.x.x,        mac=mm:aa:bb:cc:dd:ee:>    -   3. <TimeStamp=224,Event=Network, Type=TCP, Protocol=TCP,        Flags=SYN, srcip=“x.x.x.x”, srcport=mm”, destport=“aa”,        destination ip=y.y.y.y”>    -   4. <TimeStamp=225,Event=Network, Type=TCP, sub-type=connection        established (Event 023, conn established, client IP=x.x.x.x,        destination port=yy, target ip, port etc.).

Based on the response, the Bot determines or further probes to determinea set of services enabled on each of the GuestOS VMs. Bots usually probesome of the set of ports that host standard services, that is, one ormore standard, well-known ports looking for services like SMTP, IISserver, HTTP/S, FTP and so on.

The Bot tries to exploit a particular service by running a set of knownvulnerabilities against that service. If successful, the Bot tries totake control of the target host by setting up a backdoor by means of apayload drop. The payload is usually an executable program intended totake control of the target. The exploit of this vulnerability as wellthe payload drop result in generation of the following set of events. Inthis example the Bot is using a password cracking mechanism for theinitial attack vector, and then drops in a payload called mmm.exe.

-   -   1. <TimeStamp=2222, “Event”=“Network, 026”, Type=login,027>    -   2. <TimeStamp=2223, “Event”=“Authentication,31”, username=028,        password=028, authentication status=success 029>    -   3. <TimeStamp=2224, “Event”=“Network”, EventId=024, data        size=453 bytes, Event 044, file name=mmm.exe>    -   4. <TimeStamp=2225,Event=“Audit”, Audit=file created,        permission=xxx, file size=453, file-owner=root, srcip=“x.x.x.x”>    -   5. <TimeStamp=2226,Event=“APP”, AppType=FTP, srcip=“x.x.x.x”>    -   6. <TimeStamp=2227 “Event”=“OS”, Event Id=061, “Type”=“File        Store”, “File Permission”=“execute permission on file”)    -   7. <TimeStamp=2228,Event=OS”, Type=Exection,mmm.exe:>    -   8. Snort captures all connection establishments and logs them.        Individual network services or applications like FTP and HTTP        will log each of the events in /var/log/xxx.    -   9. <TimeStamp=3222,Event=“APP”, AppType=FTP, srcip=“x.x.x.x”>    -   10. <TimeStamp=3223,Event=“APP”, AppType=FTP_filedrop,        srcip=“x.x.x.x”>    -   11. <TimeStamp=3224,Event=“OS”, AppType=file stored,        srcip=“x.x.x.x”>    -   12. Some or all events are tracked/monitored as being executed        by the Bot.    -   13. <TimeStamp=4222,Event=“Audit”, AuditType=directory created,        srcip=“x.x.x.x”>    -   14. <TimeStamp=4223,Event=“OS”, command=mkdir, srcip=“x.x.x.x”>    -   15. <TimeStamp=4224,Event=“OS”, AppType=file stored,        srcip=“x.x.x.x”>

The Event Collector and Shipper 415 module transforms these as well asother events into a fixed format and sends them off to the EventCollector and Storage Engine 435. It may add relevant information like ahost name and the like.

The Event Collector and Storage Engine 435 running in the PrivilegedMode may collect some or all events from different VMs hosting differentGuestOS and feed them to MDCE 185.

The MCDE may correlate all these individual multi-dimension events,possibly in real-time, to generate one summary alert. The summary alertwill be provided to the network administrator with some or all thecritical information need to identify the Bot and the infected-targetslist. The UI module will provide the network administrator the abilityto query all the associated individual events that led to the raising ofthe summary alert and all other associated data collected as part ofindividual events.

For Example: <Event=Alert, Priority=1, Severity=1, Description=“Bot BBBdetected”, Client IP List=“x.x.x.y”, “x.x.x.x”, file drop name=mmm.exe,protocol=tcp, app=ftp, RelatedBotInfectionPhaseTransition:Timestamp1:E1, TimeStamp2:E2, TimeStamp3:E3,TimeStamp4:E4, Events=011,0222,233,233,343,234,543,2323,>

Some or all individual events that can be generated by the VMs may bemapped to one or more “Bot Infection Phase” numbers. Based on the “BotInfection Phase” number it is possible to track what phase a particularBot infection is in and monitor its progress. The “Bot Infection Phase”number transition and the associated individual events are unique foreach of the Bots and hence can be used to create a “Bot Lifecycle PhasesSignature”:

-   -   1. <scan phase=011, 022, 033>    -   2. <exploit phase=034, 0455>    -   3. <payload drop phase=0352, 0459>    -   4. <outbound scan phase=03498, 045522>    -   5. <c&c phase=02323,2988,88772>    -   6. <infect others phase=023343,54343>    -   7. <exploit traffic generation=0877,0982>

Example of BotInfectionPhaseTransition for Bot BBB may include:

-   -   1. Timestamp1:E1    -   2. TimeStamp2:E2    -   3. TimeStamp3:E3    -   4. TimeStamp4:E4,    -   5. Events=011,0222,233,233,343,234,543,2323

This “Bot Lifecycle Phases Signature” can be shared with other MDCEs,security components, end points, IPS (intrusion prevention system)devices and so on and helps them to quickly identify behaviors seen onthose systems as corresponding to Bot activity or not. Based on thisinformation, the Bot can be quickly terminated and threat mitigated, forexample, by quarantining devices whose event logs contain eventsmatching the Bot's now-known behavior. It is also possible to safeguardprivate data at the network level by configuring filters onInternet-connected routers to block all outbound traffic destined forthe C&C facility's now known address.

Some or all other security appliance/components existing in a networkcan be configured to drop and/or quarantine any traffic originating fromany of the IP addresses that have been assigned to the BotSink Appliance300. In some embodiments, a signature or schema generated according tothe methods described hereinabove may be formatted and contain some orall of the types of data in the example schema shown in FIGS. 6A and 6B.An understanding of the context in which the novel methods describedherein may be applied can be found in the following articles, which arehereby incorporated herein by reference as constituted on May 2, 2014:

https://www.gosquared.com/blog/how-to-stop-a-botnet-attack

http://www.darkreading.com/risk/up-to-9-percent-of-machines-in-an-enterprise-are-bot-infected/d/d-id/1132015?

http://rise.cse.iitm.ac.in/wiki/images/9/98/Botnet_report.pdf

http://www.cms.livjm.ac.uk/pgnet2012/Proceedings/Papers/1569604821.pdf

Referring to FIG. 7, the illustrated network environment 700 may be usedto consume dark space for one or more network resources. Security of theillustrated network environment 700 may be enhanced by monitoring thedark space within each subnet of an enterprise/cloud infrastructure forintrusions and suspicious activity and engaging the attackers usinghoneypots, such as the BotMagnet 180, MDCE 185, and Sinkhole 190. Asnoted above, the functionality of some or all of the BotMagnet 180, MDCE185, and Sinkhole 190 may be implemented in a single device such as theBotSink Appliance 300. Hereinafter, references to the “BotSink 300” mayrefer to a component implementing the functions of some or all of theBotMagnet 180, MDCE 185, and Sinkhole 190 or some or all of thefunctions of the BotSink Appliance 300.

As discussed herein “dark space” may include network resources notassigned to a particular computer system, application, or user. Inparticular, three categories are contemplated:

-   -   Dark IP Space not consumed by any device. In Information        technology dark IP space indicates IP addresses not assigned to        any device.    -   Dark Application space not consumed by any applications.        Normally applications (servers) run on specific ports and accept        incoming connections. Servers run specific applications        listening on specific ports (Default Ports Ex: HTTP Web server        port 80/443, SNMP Server 161 etc). There are lots of unused        ports that can be monitored for suspicious activity.    -   Dark user space. Most of the server applications have user login        credentials to authenticate user before allowing access to data.        If a user enters invalid or non-existent login/password        credential it results in login failure attempt and many        applications provide mechanism to log the failure to log file.

In generate, the following steps are involved in an attack orcompromising of a system:

-   -   Step-1: Exploitation and Initial Breach    -   Step-2: Reconnaissance and Extending Foothold    -   Step-4: Internal Recon and Move laterally    -   Step-3: Data Exfiltration

After an exploitation and initial breach, the attackers typically doreconnaissance to extend a foothold in the network. The goal ofreconnaissance is to learn and gather information about the systems andservices present in the network. Attackers may use various methods(stolen credentials, brute force login attempts, etc.) to gain access tosystems. The approach disclosed herein advantageously enablesidentifying of an attacker during this reconnaissance phase bymonitoring and consuming the dark IP space and diverting the attacker toany honeypot, e.g. BotSink 300, to discover the attacker's intent.

Referring specifically to FIG. 7A a corporate network may include someor all of the illustrated resources. For example, one or more VLANs 702(virtual local area networks) may be defined that include both networksand user or enterprise computer systems coupled to the networks. TheVLANs 702 may be coupled to one or more access switches 704, that inturn are coupled to a common distribution switch 706. Routing among theVLANs may be performed by the access switches 704 and distributionswitch 706. The configuration of the networking components included inVLANs 702, access switches 704, and distribution switch 706 may includeany networking component known in the art for routing date among theVLANs 702. Likewise, the access switches 704 and distribution switch 706may be replaced with any networking architecture known in the art.

Communication between the VLANs 702 and external networks may occurthrough a core switch 708 and a firewall 710. As illustrated thedistribution switch 706 may be coupled to the core switch 708, which iscoupled to the firewall 710. The firewall 710 may be coupled to a widearea network (WAN), such as the Internet, or some other network. Asknown in the art a firewall 710 may implement policies reducingmalicious access of internal networking devices, such as the VLANs 702.

External computing devices 714 may access services provided by thenetwork environment 700 through the WAN 712 and firewall 710. Likewise,attempts to compromise the network environment 700 by attacker systems716 may occur over the WAN 712 and be routed through the firewall 710.

The network environment 700 may further include other componentsimplementing services provided to external computing devices 714. Forexample, a data center 718 may host date served to external computingdevices 714. A DMZ (demilitarized zone) server farm 720 may implementother services accessible by external computing devices. As shown, boththe datacenter 718 and server farm 720 may be accessed by externalcomputing devices 714 through the firewall 710.

The BotSink 300 may monitor traffic and receive traffic from thedistribution switch 706 in order to identify dark space in the VLANs 702and detect attempts to access the dark space. In some embodiments, theBotSink 300 also receives traffic from non-routable networks, e.g. darksubnets that do not include any assigned IP addresses or network devicesrouting traffic thereto. For example, a distribution switch 706,firewall 710, or other network device forming the network environment700 may forward traffic for such dark subnets to the BotSink 300. TheBotSink 300 may then perform DNAT (destination network addresstranslation) and engage the attacker, such as according to any of themethods described herein. The resources of a dark space that traffic mayattempt to access may include some or all of dark IP space, darkapplication space, and dark user space as described in detail herein.

Referring to FIG. 7B, in other embodiments, the BotSink may monitortraffic and received traffic from the access switches 704, rather thanfrom the distribution switch 706. The network environments 700 shown inFIGS. 7A and 7B are just two examples of configurations that may beused. A BotSink 300 may monitor and receive traffic from various sourcesfrom various configurations in order to implement the methods disclosedherein.

Referring to FIG. 8, a system 800 may include hosts 802 a, 802 b thatare computer systems of one or more of the VLANs 702. The hosts 802 a,802 b may host locally or network accessible applications. In theillustrated embodiment, the hosts 800 a, 800 b host one or more virtualmachines 804 a, 804 b. The hosts 802 a, 802 b may be implement virtualextensible local area network (VXLAN) tunnel end points (VTEP) 806 a,806 b in order to communicate over a VXLAN 808. In otherimplementations, the hosts 802 a, 802 b may communicate with a LAN usingother means known in the art.

In some embodiments, the hosts 802 a, 802 b communicate with the VXLAN808 through a virtual distribution switch 810. Other interfaces betweenthe hosts 802 a, 802 b and the VXLAN, or other type of network may beused as known in the art.

In order to detect access attempts for non-existent IP's the system 800,such as by means of the BotSink 300, monitors some or all broadcast andmulticast methods in each subnet, e.g. VLAN 702, protected according tothe approach described herein. The BotSink 300 processes DHCP (dynamichost configuration protocol) and ARP (address resolution protocol)packets in order to build an IP address to MAC address mapping table812. For hosts that have static IP assigned, the BotSink 300 monitorsARP requests originated from that host to learn the IP address assigned.The BotSink uses the IP to MAC (media access code) address table 812 andchecks if an ARP request for a device is sent that includes anon-existent IP address, e.g. one that has not been authenticallyassigned to a host of a VLAN 702.

Virtual extensible local area network (VXLAN) is a networkvirtualization technology that is built on top of existing Layer 2 andLayer 3 technologies to provide higher scalability. VLANs have a limitedscalability of 4096 networks and VXLANs can increase the number oflogical networks to 16 million. To support scaling the number of VXLANnetworks, the SDN (Software Defined Networking) switches support ARPsuppression to limit the number of broadcast messages sent in a VXLANnetwork. The SDN controller uses various methods to determine IP to Macaddress table mappings and builds those tables. In some embodiments,table 812 may be populated by the BotSink in cooperation with these SDNcontrollers to learn the IP to MAC address table mappings using theirexternal programming interface. Once the BotSink detects an accessattempt is being made for dark IP it consumes, i.e. assigns, the IPusing DHCP/Static methods to the BotSink 300 and engages the attacker,such as according to some or all of the approaches described herein.

As is apparent in FIG. 8, the table 812 may include for each entry a MACaddress field 814 for a computer system or VM executing on a computersystem, an IP address for the computer system or VM, and a VXLAN field818 that indicates the VXLAN in which that mapping was detected.

The BotSink 300 may interface with the virtual distribution switch 810of the VXLAN in order to detect ARP requests and determine those thatreference an unallocated IP address for that VXLAN. For example,referring to FIG. 9, the illustrated method 900 may be executed by aBotSink 300 or some other computer system implementing security in anetwork environment, such as in the environment 700 of FIGS. 7A and 7Band including some or all of the components of the system 800 of FIG. 8.

The method 900 may include monitoring 902 broadcasting of DHCP and ARPpackets within a network domain. In particular, broadcast and multicastpackets according to either protocol may be monitored in order todetermine IP addresses assigned to particular computer systems andnetwork components and the MAC addresses mapped to these addresses. Asknown in the art, computer systems in a network may broadcast a mappingof an IP address to a MAC address according to either of the DHPC or ARPprotocols. The method 900 may further include receiving 904 from an SDNcontroller of a VXLAN a MAC-to-IP address table. The MAC-to-IP addressmappings received at one or both of steps 902 and 904 may be stored 906by the BotSink 300. For example, a single table may be stored 906 foreach network, e.g. VLAN or VXLAN include all mappings obtained for thatnetwork, at either step 902 or step 904. In some embodiments, mappingsmay be obtained using only one of steps 902 or step 904 for a givennetwork.

The method 900 may further include monitoring 908 DHCP and ARP packets908. If a DHCP or ARP packet, such as an ARP request, is found 910 toreference an unallocated IP address not found in the MAC-to-IP addresstable, then the method 900 may include allocating 912 that IP address tothe BotSink 300, such as by assigning the IP address detected at step910 to a VM executing on the BotSink 300. The VM may implement one ormore services such that requests including the IP address detected atstep 910 may be responded to in a realistic manner. In particular, theVM may respond according to any of the methods described herein forengaging an attacker. Allocating 912 the unallocated IP address fromstep 910 may include making a static assignment of the unallocated IPaddress to the BotSink within the network, such as by issuing an ARPbroadcast indicating the assignment.

The method 900 may include routing 914 packets including the unallocatedIP address detected at step 910 to the BotSink 300. For example,according to the allocation step of step 912, requests including theunallocated IP address will be routed by the network devices of anetwork (e.g. switches, routers, etc.) to the BotSink 300. In responseto receiving the packets, the BotSink 300 may execute commands orrequests included in the packets and monitor and report 916 one or bothof the packets and the actions invoked thereby, such as to an MDCE 185.In response to the packets, the BotSink 300 may engage an attackersystem 716 that originated the packets according to any of the methodsdisclosed herein. In particular, in response to determining that theattacker system 716 is performing malicious activities, access toresources of the network environment 700 by the attacker system 716 maybe blocked and/or executable code provided by the attacker system 716may be reported to computing devices of the network environment 700 asbeing malicious.

Referring to FIG. 10, the illustrated system 1000 illustrates componentsthat may be used to monitoring access of an application dark space. Thesystem 1000 includes some of the components of the system 800 andmonitoring of access to an application dark space may be performed inparallel with monitoring access to an IP dark space as described above.The illustrated system 1000 is one example configuration of a system formonitoring access to an application dark space and the methods disclosedherein may be implemented in other network configurations known in theart.

The system 1000 may include a virtual distribution firewall 1002interposed between one or more hosts 802 a, 802 b and the virtualdistribution switch. The virtual switch 810 may be coupled to one ormore switches 1004 a-1004 d that are interposed between a virtualdistribution switch 810 and an aggregation switch and firewall 1006. Todetect unauthorized access attempts for non-existent ports the systemprograms an SDN controller or firewall, such as firewall 1006, to sendselect packet sequences to the BotSink 300 (e.g., TCP SYN and TCP RSTpackets). The BotSink 300 may then monitor probes for hosts looking fornon-existent applications by analyzing the select packet sequencesreceived from the firewall 1006.

The BotSink 300 may then learn the application port being probed by theselect packets and launch that application on that port within theBotSink 300, such as VM executing on the BotSink 300. For example, iftraffic is destined for port 80 that has not been allocated and which isusually assigned to a web application server, the BotSink 300 may launcha web application server associated with port 80 of the BotSink 300 or aVM executing thereon. The application launched in response to probingfor an unallocated port may also be specified by a user, such as in aconfiguration file accessed by the BotSink 300. In case of SDN virtualswitches, the BotSink 300 may program the SDN controller thereof toapply NAT (Network Address Translation) rules and redirect traffic fornon-existent applications to the BotSink 300.

Referring to FIG. 11, the illustrated method 1100 may be executed by aBotSink 300 or some other computer system implementing security in anetwork environment, such as in the environment 700 of FIGS. 7A and 7Band including some or all of the components of the system 1000 of FIG.10.

The method 1100 may include programming 1102 a firewall to send selectpacket sequences to the BotSink 300. For example, the BotSink 300 mayprogram 1102 the firewall to transmit copies of TCP SYN and TCP RSTpackets to the BotSink 300. The BotSink 300 may then monitor 1104 theselect packet sequences received from the firewall and evaluate 1106whether the select packet sequences indicate an attempt to access anunallocated port of one or more computer systems of a network locatedbehind the firewall. If the select packet sequences are found 1106 toindicate an attempt to communicate with an unallocated port, the method1100 may include allocating 1108 the unallocated port in the BotSink300, such as by one or both of instantiating a VM and allocating a portof the VM on the BotSink 300. Likewise, an application corresponding tothe port number of the unallocated port from step 1106 may beinstantiated and initiated on the BotSink 300, such as within the VMexecuting on the BotSink 300. In some embodiments, the BotSink 300 mayperform a port-scan across some or all subnets of a network locatedbehind the firewall. The BotSink 300 may learn from the port scan theapplications and/or ports that are used by each device of the network.The BotSink 300 may then program NAT rules of a firewall and/or othernetwork routing devices to redirect traffic for unused ports across eachdevice to the BotSink 300.

In many cases, each port number is associated with a particularapplication across all nodes, i.e. as a convention followed by mostcomputer networks. Accordingly, the application instantiated andinitiated at step 1110 may include the application associated with thenumber of the unallocated port according to convention. Alternatively,an administrator of the BotSink 300 may specify an alternative mappingof a port number to a different application, such that that differentapplication will be instantiated at step 1110.

The method 1100 may further include programming a network addresstranslation (NAT) component of the firewall, an aggregation switch,virtual distribution, switch, or other network component, to routepackets including the unallocated port to the BotSink 300. For example,the select packet referencing the unallocated port may include an IPaddress and a port number. If the port number for that IP address is notassigned, then NAT rules may be programmed to route subsequent packetsto that combination of IP address and port number to the BotSink 300.

In response to receiving the packets routed thereto per step 1114, theBotSink 300 may execute commands or requests included in the packets andmonitor and report 1116 one or both of the packets and the actionsinvoked thereby, such as to an MDCE 185. In response to the packets, theBotSink 300 may engage an attacker system 716 that originated thepackets according to any of the methods disclosed herein. In particular,in response to determining that the attacker system 716 is performingmalicious activities, access to resources of the network environment 700by the attacker system 716 may be blocked and/or executable codeprovided by the attacker system 716 may be reported to computing devicesof the network environment 700 as being malicious.

Referring to FIG. 12, the illustrated system 1200 illustrates componentsthat may be used to monitoring access of a user dark space. The system1200 includes some of the components of the systems 800 and 1000 andmonitoring of access to a user dark space may be performed in parallelwith monitoring access to an IP dark space and application dark space asdescribed above. The illustrated system 1200 is one exampleconfiguration of a system for monitoring access to a user dark space andthe methods disclosed herein may be implemented in other networkconfigurations known in the art.

The system 1200 may integrate with enterprise SIEM (security informationand event management)/Syslog server or the servers may be programmed tosend application logs to honeypot. For example, a system log collector300 may be implemented by an application executing on a server of anetwork may implement a system log collector 1202 that collects errormessages from applications executing on the server. Alternatively, thesystem log collector 1202 may be executed on a first server system andreceive application logs from applications on executing on one or bothof the first server system and other server systems. In either case, thesystem log collector 1202 may interface with the BotSink 300 and provideerror logs thereto.

The BotSink 300 may then monitor the application logs for login anomalyattempts (i.e. some host trying to log into a application usingnon-existent account or invalid password). The BotSink may furthermonitor the application logs for attempts to access non-existent webHTML pages. In HTTP protocol the response “404 Not Found” indicates someone accessing an non-existent page.

In response, the BotSink 300 may program some or all of a SDN controlleror firewall, e.g. the aggregation distribution firewall 1002, to applyNAT rules to route subsequent requests from the source of the loginanomaly or access attempt to the BotSink 300. For example, the BotSink300 may program the virtual distribution firewall 1002 using a 4 tupleACL rule (ex: Destination IP, Destination Port, Src IP, Protocol), wheredestination IP and destination port refer to the BotSink 300 and Src IPis a source IP of the login anomaly or request for a non-existent HTMLPage. The 4 tuple ACL rule will then cause the virtual distributionfirewall 1002 to redirect subsequent traffic to the BotSink 300.

In some embodiment, the BotSink 300 may further be programmed to allow anumber of unsuccessful login attempts or attempts to access non-existentHTML pages from a source host before diverting traffic from that sourceto the BotSink 300 and engaging the source host according to the methodsdescribed herein.

Various deployment options and various methods may be used to redirecttraffic to the BotSink 300 once a suspicious host is detected. Inparticular, the BotSink 300 may interface with various SDN switches,firewalls, and the like, in order to program NAT rules to achieverouting of packets from suspicious hosts to the BotSink 300.

Referring to FIG. 13, the illustrated method 1300 may be executed by aBotSink 300 or some other computer system implementing security in anetwork environment, such as in the environment 700 of FIGS. 7A and 7Band including some or all of the components of the system 1200 of FIG.12.

The method 1300 may include integrating 1302 with a source ofapplication logs in order to obtain updates to the application logs.This may include integrating with an SIEM/Syslog server or directly withthe applications to receive output events as they occur.

The BotSink 300 may then monitor 1304 the application logs received andevaluate 1306 whether the application logs indicate one or more attemptsfrom a source host to access a non-existent username, access anon-existent HTML resource, or login with an incorrect password. In someembodiments, the method 1300 may determine whether N of such attemptswere made, where N is some value greater than 1 and may be large enoughto give an impression of a successful brute force attack, e.g. severalhundred or several thousand. The value of N may be random, i.e. for eachsource host, the number N at which the condition of step 1306 issatisfied may be selected according to a random or pseudo randomfunction.

If the application logs are found to meet the condition of step 1306,then the method 1300 may include instantiating 1308 on the BotSink 300an instance of the application for which the unsuccessful attempt orattempts to login were made. Where the unsuccessful attempts wereattempts to access a resource, such as an HTML page, a web server orother application corresponding to that resource may be instantiated atstep 1308. In some instances, an instance of the application may alreadybe executing on the BotSink 300, in which case step 1308 may be omitted.

The method 1300 may further include creating 1310 an account in theapplication the source host attempted to access. In particular, where Naccess attempts to login have been made, an account may be created atstep 1310 that includes the username and/or password included in the Nthlogin attempt. Where the access attempt is for a resource, that resourcemay be created on the BotSink 300 and given a name or other identifierincluded in the attempted access from the source host.

The method 1300 may further include programming NAT rules in some or allof a firewall, SDN controller, or other network device to route packetsfrom the source host that generated the unsuccessful attempts detectedat step 1306 to the BotSink 300. Subsequent packets from that sourcehost may then be routed 1314 to the BotSink 300.

If the select packet sequences are found 1106 to indicate an attempt tocommunicate with an unallocated port, the method 1100 may includeallocating 1108 the unallocated port in the BotSink 300, such as by oneor both of instantiating a VM and allocating a port of the VM on theBotSink 300. Likewise, an application corresponding to the port numberof the unallocated port from step 1106 may be instantiated and initiatedon the BotSink 300, such as within the VM executing on the BotSink 300.

In response to receiving the packets routed thereto per step 1314, theBotSink 300 may execute commands or requests included in the packets andmonitor and report 1316 one or both of the packets and the actionsinvoked thereby, such as to an MDCE 185. In response to the packets, theBotSink 300 may engage an attacker system 716 that originated thepackets according to any of the methods disclosed herein. In particular,in response to determining that the attacker system 716 is performingmalicious activities, access to resources of the network environment 700by the attacker system 716 may be blocked and/or executable codeprovided by the attacker system 716 may be reported to computing devicesof the network environment 700 as being malicious.

FIG. 14 is a block diagram illustrating an example computing device 1400which can be used to implement the BotMagnet 180, the MDCE 185, 187, or188, the Sinkhole 190, the Management Server or Monitor 195, the BotSinkAppliance 300, access switch 704, distribution switch 706, core switch708, firewall 710, user system 714, attacker system 716, and datacenterserver 718. In some embodiments, a cluster of computing devicesinterconnected by a network may be used to implement these components ofthe invention. For example, a cluster could be used for large-scaleservices such as a higher-level MDCE 187 or a “global” MDCE 188. Thiscould also be true for the Sinkhole 190, which could be acluster/service shared by all of the BotMagnets 180 in a local network.

Computing device 1400 may be used to perform various procedures, such asthose discussed herein. Computing device 1400 can function as a server,a client, or any other computing entity. Computing device can performvarious monitoring functions as discussed herein, and can execute one ormore application programs, such as the application programs describedherein. Computing device 1400 can be any of a wide variety of computingdevices, such as a desktop computer, a notebook computer, a servercomputer, a handheld computer, tablet computer and the like.

Computing device 1400 includes one or more processor(s) 1402, one ormore memory device(s) 1404, one or more interface(s) 1406, one or moremass storage device(s) 1408, one or more Input/Output (I/O) device(s)1410, and a display device 1430 all of which are coupled to a bus 1412.Processor(s) 1402 include one or more processors or controllers thatexecute instructions stored in memory device(s) 1404 and/or mass storagedevice(s) 1408. Processor(s) 1402 may also include various types ofcomputer-readable media, such as cache memory.

Memory device(s) 1404 include various computer-readable media, such asvolatile memory (e.g., random access memory (RAM) 1414) and/ornonvolatile memory (e.g., read-only memory (ROM) 1416). Memory device(s)1404 may also include rewritable ROM, such as Flash memory.

Mass storage device(s) 1408 include various computer readable media,such as magnetic tapes, magnetic disks, optical disks, solid-statememory (e.g., Flash memory), and so forth. As shown in FIG. 14, aparticular mass storage device is a hard disk drive 1424. Various drivesmay also be included in mass storage device(s) 1408 to enable readingfrom and/or writing to the various computer readable media. Mass storagedevice(s) 1408 include removable media 1426 and/or non-removable media.

I/O device(s) 1410 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 1400.Example I/O device(s) 1410 include cursor control devices, keyboards,keypads, microphones, monitors or other display devices, speakers,printers, network interface cards, modems, lenses, CCDs or other imagecapture devices, and the like.

Display device 1430 includes any type of device capable of displayinginformation to one or more users of computing device 1400. Examples ofdisplay device 1430 include a monitor, display terminal, videoprojection device, and the like.

Interface(s) 1406 include various interfaces that allow computing device1400 to interact with other systems, devices, or computing environments.Example interface(s) 1406 include any number of different networkinterfaces 1420, such as interfaces to local area networks (LANs), widearea networks (WANs), wireless networks, and the Internet. Otherinterface(s) include user interface 1418 and peripheral device interface1422. The interface(s) 1406 may also include one or more user interfaceelements 1418. The interface(s) 1406 may also include one or moreperipheral interfaces such as interfaces for printers, pointing devices(mice, track pad, etc.), keyboards, and the like.

Bus 1412 allows processor(s) 1402, memory device(s) 1404, interface(s)1406, mass storage device(s) 1408, and I/O device(s) 1410 to communicatewith one another, as well as other devices or components coupled to bus1412. Bus 1412 represents one or more of several types of busstructures, such as a system bus, PCI bus, IEEE 1394 bus, USB bus, andso forth.

For purposes of illustration, programs and other executable programcomponents are shown herein as discrete blocks, although it isunderstood that such programs and components may reside at various timesin different storage components of computing device 1400, and areexecuted by processor(s) 1402. Alternatively, the systems and proceduresdescribed herein can be implemented in hardware, or a combination ofhardware, software, and/or firmware. For example, one or moreapplication specific integrated circuits (ASICs) can be programmed tocarry out one or more of the systems and procedures described herein.

What is claimed is:
 1. A method for detecting unauthorized access of anetwork environment, the method comprising: monitoring, by a securitycomputer system, access of authentically allocated network resources;detecting, by security computer system, one or more access requestsreferencing one or more dark space resources, the one or more dark spaceresources being network resources that have not been allocatedauthentically within the network environment; in response to detectingthe one or more access requests— allocating, by security computersystem, to a decoy system the one or more dark space resources; routing,by security computer system, at least one of the one or more accessrequests and a subsequent request referencing the one or more dark spaceresources to the decoy system; monitoring, by security computer system,actions taken on the decoy system responsive to the at least one of theone or more access requests and the subsequent request; determining, bysecurity computer system, that the actions taken on the decoy systemindicate malicious activity; in response to determining that the actionstaken on the decoy system indicate malicious activity, instructing oneor more computer systems of the network environment to block access by asource of the one or more access requests.
 2. The method of claim 1,further comprising, in response to detecting the one or more accessrequests, instantiating within a virtual machine executing on the decoysystem a computing resource referenced in the access request.
 3. Themethod of claim 1, wherein monitoring, by the security computer system,access of authentically allocated network resources comprises: detectingdynamic host configuration protocol (DHCP) packets and addressresolution protocol (ARP) packets that are included in at least one ofbroadcast and multicast transmissions in the network environment;extracting from the DHCP packets and ARP packets mappings of mediaaccess code (MAC) addresses to internet protocol (IP) addresses; andstoring the mappings in a table;
 4. The method of claim 3, whereindetecting the one or more access requests referencing the one or moredark spaces resources comprises: detecting packet traffic in the networkenvironment; detecting unallocated IP addresses in the packet traffic,the unallocated IP addresses not included in the table; determining thata portion of the packet traffic referencing unallocated IP addresses arethe one or more access requests referencing the dark space resource. 5.The method of claim 1, wherein monitoring, by the security computersystem, access of authentically allocated network resources comprises:receiving, from a firewall system within the network environment reportsof packet sequences including at least one of transport control protocol(TCP) SYN and RST packets.
 6. The method of claim 5, wherein detectingthe one or more access requests referencing the one or more dark spaceresources comprises: determining that the reports of packet sequencesindicate one or more requests to access an unallocated port within thenetwork environment.
 7. The method of claim 6, further comprising, inresponse to determining that the reports of packet sequences indicateone or more requests to access an unallocated port within the networkenvironment, allocating in the decoy system the unallocated port andinstantiating on the decoy system an application corresponding to a portnumber of the unallocated port.
 8. The method of claim 1, whereinmonitoring, by the security computer system, access of authenticallyallocated network resources comprises: receiving application logs fromone or more applications executing in the network environment.
 9. Themethod of claim 8, wherein detecting the one or more access requestsreferencing the one or more dark space resources comprises: determiningthat the application logs indicate one or more attempts to access one ormore applications using one or more invalid usernames.
 10. The method ofclaim 9, further comprising, in response to determining that theapplication logs indicate one or more attempts to access the one or moreapplications using the one or more invalid usernames: creating, on thedecoy system, one or more user accounts having as usernames the one ormore invalid usernames; and allowing, on the decoy system, access to theone or more applications using the one or more invalid usernames inresponse to at least one of the one or more access requests and one ormore subsequent requests including the one or more invalid usernames.11. A system for detecting unauthorized access, the system comprising: anetwork environment including one or more computer systems and one ormore internal networks coupling the one or more deployed computersystems to an external network; a security computer system one or moreprocessors and one or more memory devices, the one or more memorydevices storing executable and operational code effective to cause theone or more processors to— monitor access of authentically allocatednetwork resources within the one or more internal networks; if one ormore access requests referencing one or more dark space resources aredetected, the one or more dark space resources being network resourcesof the internal network and the one or more deployed computer systemsthat have not been allocated authentically: allocated the one or moredark space resources to the security computer system; invoke routing ofat least one of the one or more access requests and a subsequent requestreferencing the one or more dark space resources to the securitycomputer system; monitor actions taken on the security computer systemresponsive to the at least one of the one or more access requests andthe subsequent request; determine that the actions taken on the decoysystem indicate malicious activity; in response to determining that theactions taken on the decoy system indicate malicious activity, instructthe one or more deployed computer systems of the network environment toblock access by a source of the one or more access requests.
 12. Thesystem of claim 11, wherein the executable and operational code arefurther effective to cause the one or more processors to, instantiatewithin a virtual machine executing on the security computer system acomputing resource referenced in the access request if the one or moreaccess requests referencing the one or more dark resources are detected.13. The system of claim 11, wherein the executable and operational codeare further effective to cause the one or more processors to monitoraccess of authentically allocated network resources by: detectingdynamic host configuration protocol (DHCP) packets and addressresolution protocol (ARP) packets that are included in at least one ofbroadcast and multicast transmissions in the network environment;extracting from the DHCP packets and ARP packets mappings of mediaaccess code (MAC) addresses to internet protocol (IP) addresses; andstoring the mappings in a table;
 14. The system of claim 13, wherein theexecutable and operational code are further effective to cause the oneor more processors to detect the one or more access requests referencingthe one or more dark spaces resources by: detecting packet traffic inthe network environment; detecting unallocated IP addresses in thepacket traffic, the unallocated IP addresses not included in the table;if a portion of the packet traffic reference unallocated IP addresses,determined that the portion of the packet traffic is the one or moreaccess requests referencing the dark space resource.
 15. The system ofclaim 11, wherein the executable and operational code are furthereffective to cause the one or more processors to monitor access ofauthentically allocated network resources by: receiving, from a firewallsystem within the network environment reports of packet sequencesincluding at least one of transport control protocol (TCP) SYN and RSTpackets.
 16. The system of claim 15, wherein the executable andoperational code are further effective to cause the one or moreprocessors to: if the reports of packet sequences indicate one or morerequests to access an unallocated port within the network environment,determine that the reports of packet sequences include one or moreaccess requests referencing the one or more dark space resources. 17.The system of claim 16, wherein the executable and operational code arefurther effective to cause the one or more processors to: if the reportsof packet sequences indicate one or more requests to access anunallocated port within the network environment, allocate theunallocated port and instantiate an application corresponding to a portnumber of the unallocated port.
 18. The system of claim 11, wherein theexecutable and operational code are further effective to cause the oneor more processors to monitor access of authentically allocated networkresources by: receiving application logs from one or more applicationsexecuting in the network environment.
 19. The system of claim 18,wherein the executable and operational code are further effective tocause the one or more processors: if the application logs indicate oneor more attempts to access one or more applications using one or moreinvalid usernames, determine that the one or more access requestsreference the one or more dark space resources.
 20. The system of claim19, wherein the executable and operational code are further effective tocause the one or more processors to, if the application logs indicateone or more attempts to access the one or more applications using theone or more invalid usernames: create one or more user accounts havingas usernames the one or more invalid usernames; and allow access to theone or more applications using the one or more invalid usernames inresponse to at least one of the one or more access requests and one ormore subsequent requests including the one or more invalid usernames.